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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Perceiver/Fine_tune_Perceiver_for_text_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycx34gWpcZn7"
      },
      "source": [
        "## Set-up environment\n",
        "\n",
        "As usual, we first install HuggingFace Transformers, and Datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "rpak2KOoKqOg",
        "outputId": "b4bb1ceb-1b2e-40ed-c52a-2e3ef2152138"
      },
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'transformers': No such file or directory\n",
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 95370, done.\u001b[K\n",
            "remote: Counting objects: 100% (17/17), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 95370 (delta 5), reused 15 (delta 5), pack-reused 95353\u001b[K\n",
            "Receiving objects: 100% (95370/95370), 72.35 MiB | 28.46 MiB/s, done.\n",
            "Resolving deltas: 100% (68666/68666), done.\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 895 kB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 51.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 53.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 61 kB 616 kB/s \n",
            "\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHYPlPYccrwU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54ec2103-afd3-4895-f18c-cfd1a3e2230d"
      },
      "source": [
        "!pip install -q datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 298 kB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 55.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 132 kB 73.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 243 kB 72.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 160 kB 69.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 48.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 192 kB 49.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ltlte3TacqGC"
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      "source": [
        "## Prepare data\n",
        "\n",
        "Here we take a small portion of the IMDB dataset, a binary text classification dataset (\"is a movie review positive or negative?\")."
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        "id": "vGSKcPLEcd1J",
        "outputId": "9c226590-db13-482f-eafe-dbd4a73f7fef"
      },
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_ds, test_ds = load_dataset(\"imdb\", split=['train[:10]+train[-10:]', 'test[:5]+test[-5:]'])"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "model_id": "53f5ae6d8e4d4152b56b5ec52a119043",
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            "text/plain": [
              "Downloading:   0%|          | 0.00/1.79k [00:00<?, ?B/s]"
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              "Downloading:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1...\n"
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              "Downloading:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"
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              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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          },
          "metadata": {}
        },
        {
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              "0 examples [00:00, ? examples/s]"
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              "0 examples [00:00, ? examples/s]"
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        {
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          "name": "stdout",
          "text": [
            "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1. Subsequent calls will reuse this data.\n"
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              "  0%|          | 0/2 [00:00<?, ?it/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "We create id2label and label2id mappings, which are handy at inference time."
      ],
      "metadata": {
        "id": "psAnJj0MVgTQ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2Waba6gcs36",
        "outputId": "19506141-0b11-4504-95c7-4928c6cc33ed"
      },
      "source": [
        "labels = train_ds.features['label'].names\n",
        "print(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['neg', 'pos']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "Ex0KxN6pcwHS",
        "outputId": "9cb7f370-fb19-4f4b-ebd3-696721c2c18e"
      },
      "source": [
        "id2label = {idx:label for idx, label in enumerate(labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}\n",
        "print(id2label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'neg', 1: 'pos'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we prepare the data for the model using the tokenizer. "
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            "7b98f62dde3a4de88206c211c1169513",
            "199b95dc25dd49a6b1e56c4da001c19d",
            "65dddf6ebdf7493797bd5de7cc887da3",
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            "d09eb19733384b2e9cb1285eafac2f48",
            "4f9cafa611194b439e130633f4ef896e",
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            "23dccbf28b1f4aaa8c66b61d13180498",
            "467db5f23a984110a618ee50c7ae0078",
            "59ff08fad3e745b082463644923f24b3",
            "7baf65ab24cc4eb995c08a63c7be9867",
            "2487457c396a4e7b956f13fb6e69b306",
            "3329c207fdcb404882d7000890271f62",
            "706f3b5ac7024c49b5ff927fa6fdac48",
            "dfc3b45635ac42fb8d9dbce3f6a5c331",
            "030c251a261a469f9b2efa56b21c3e40",
            "d5066921e6ec4627acd94ef1e5074cc6",
            "3b749d8fbefa46c29991d5aba1804adf",
            "fa7bcc849e51411e915956f22e679501",
            "2341b196aed2484388a0328515aa3c4f",
            "b73ce8401cfa4f41b0073c66dab15d2c",
            "1d894e1d91df4cd0811283f8fff6f8e7",
            "d94b3beffc2040d483b7c5e8b8d9d9b4",
            "ba6d253d7a3b4d9fad0027f6913b6898"
          ]
        },
        "id": "cQphEjiAcxP6",
        "outputId": "bd3f5bb9-4747-45af-ab7d-62aaca02cd3e"
      },
      "source": [
        "from transformers import PerceiverTokenizer\n",
        "\n",
        "tokenizer = PerceiverTokenizer.from_pretrained(\"deepmind/language-perceiver\")\n",
        "\n",
        "train_ds = train_ds.map(lambda examples: tokenizer(examples['text'], padding=\"max_length\", truncation=True),\n",
        "                        batched=True)\n",
        "test_ds = test_ds.map(lambda examples: tokenizer(examples['text'], padding=\"max_length\", truncation=True),\n",
        "                      batched=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b98f62dde3a4de88206c211c1169513",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/823 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7bbd255eb3640bfbf54799b84dddb0a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/911 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n",
            "Using unk_token, but it is not set yet.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "87813f2da6b141ffa2d45cd7e93c0cc0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "706f3b5ac7024c49b5ff927fa6fdac48",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We set the format to PyTorch tensors, and create familiar PyTorch dataloaders."
      ],
      "metadata": {
        "id": "-nrT_MtEVnkk"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQ7Aa2KudFO1"
      },
      "source": [
        "train_ds.set_format(type=\"torch\", columns=['input_ids', 'attention_mask', 'label'])\n",
        "test_ds.set_format(type=\"torch\", columns=['input_ids', 'attention_mask', 'label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pEiNbDMdJJ0"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(train_ds, batch_size=4, shuffle=True)\n",
        "test_dataloader = DataLoader(test_ds, batch_size=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we verify some things (always important to check out your data!)."
      ],
      "metadata": {
        "id": "HivHZS1wVsCj"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2heqUKfedOt-",
        "outputId": "564265ee-957c-4257-a94a-e46a4d8596e7"
      },
      "source": [
        "batch = next(iter(train_dataloader))\n",
        "for k,v in batch.items():\n",
        "  print(k,v.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attention_mask torch.Size([4, 2048])\n",
            "input_ids torch.Size([4, 2048])\n",
            "label torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "d_tt4gIUdP3s",
        "outputId": "4919a964-74d5-4c11-eddc-f33eeaf3f547"
      },
      "source": [
        "tokenizer.decode(batch['input_ids'][3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Very smart, sometimes shocking, I just love it. It shoved one more side of David's brilliant talent. He impressed me greatly! David is the best. The movie captivates your attention for every second.<pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pa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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDVhJ1nVg5d4",
        "outputId": "e4b792dc-d50c-429b-e067-1c59ead7ef4a"
      },
      "source": [
        "batch['label']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DixaPHgLdUMG"
      },
      "source": [
        "## Define model\n",
        "\n",
        "Next, we define our model, and put it on the GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a40a1dde10dc4e69aae8ce9fe976627e",
            "bf5884a45a86430db915eeca318bf849",
            "c1b3306ae38c4fe79695a69363443698",
            "ebc790877dbc402bb5a260bedec83375",
            "6646939af1a843e0a84559b4a9a5e92b",
            "f9b79b9a38564c32b62a8aa3e537e11f",
            "993fb5edeb6c44f584d7660a1473cfdc",
            "53f5c8769bfc4d79bf1e58525c327922",
            "d0ef1d94c939436391353698617e9f17",
            "312c8ca4ea4b450b9c56bafda2767051",
            "f24d12bbe3884372bb4abd292d2b53aa"
          ]
        },
        "id": "sjd2opzAdRQd",
        "outputId": "f2b9314d-cfd2-4718-e231-8a48309c322a"
      },
      "source": [
        "from transformers import PerceiverForSequenceClassification\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = PerceiverForSequenceClassification.from_pretrained(\"deepmind/language-perceiver\",\n",
        "                                                               num_labels=2,\n",
        "                                                               id2label=id2label,\n",
        "                                                               label2id=label2id)\n",
        "model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a40a1dde10dc4e69aae8ce9fe976627e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/767M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at deepmind/language-perceiver were not used when initializing PerceiverForSequenceClassification: ['perceiver.decoder.decoding_cross_attention.attention.self.layernorm1.bias', 'perceiver.decoder.decoding_cross_attention.mlp.dense1.bias', 'perceiver.decoder.decoding_cross_attention.attention.output.dense.bias', 'perceiver.decoder.decoding_cross_attention.attention.self.key.weight', 'perceiver.decoder.decoding_cross_attention.attention.self.layernorm1.weight', 'perceiver.decoder.decoding_cross_attention.attention.self.query.bias', 'perceiver.decoder.decoding_cross_attention.attention.self.key.bias', 'perceiver.decoder.decoding_cross_attention.attention.self.layernorm2.bias', 'embedding_decoder.bias', 'perceiver.decoder.output_position_encodings.position_embeddings', 'perceiver.decoder.decoding_cross_attention.mlp.dense2.bias', 'perceiver.decoder.decoding_cross_attention.mlp.dense1.weight', 'perceiver.decoder.decoding_cross_attention.attention.output.dense.weight', 'perceiver.decoder.decoding_cross_attention.attention.self.query.weight', 'perceiver.decoder.decoding_cross_attention.attention.self.value.bias', 'perceiver.decoder.decoding_cross_attention.attention.self.layernorm2.weight', 'perceiver.decoder.decoding_cross_attention.mlp.dense2.weight', 'perceiver.decoder.decoding_cross_attention.layernorm.bias', 'perceiver.decoder.decoding_cross_attention.layernorm.weight', 'perceiver.decoder.decoding_cross_attention.attention.self.value.weight']\n",
            "- This IS expected if you are initializing PerceiverForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing PerceiverForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of PerceiverForSequenceClassification were not initialized from the model checkpoint at deepmind/language-perceiver and are newly initialized: ['perceiver.decoder.decoder.decoding_cross_attention.attention.self.layernorm2.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.key.weight', 'perceiver.decoder.decoder.decoding_cross_attention.layernorm.weight', 'perceiver.decoder.decoder.decoding_cross_attention.attention.output.dense.weight', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.value.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.layernorm2.weight', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.value.weight', 'perceiver.decoder.decoder.output_position_encodings.position_embeddings', 'perceiver.decoder.decoder.decoding_cross_attention.mlp.dense2.bias', 'perceiver.decoder.decoder.decoding_cross_attention.mlp.dense2.weight', 'perceiver.decoder.decoder.final_layer.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.layernorm1.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.key.bias', 'perceiver.decoder.decoder.decoding_cross_attention.layernorm.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.output.dense.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.query.weight', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.layernorm1.weight', 'perceiver.decoder.decoder.final_layer.weight', 'perceiver.decoder.decoder.decoding_cross_attention.mlp.dense1.bias', 'perceiver.decoder.decoder.decoding_cross_attention.attention.self.query.bias', 'perceiver.decoder.decoder.decoding_cross_attention.mlp.dense1.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PerceiverForSequenceClassification(\n",
              "  (perceiver): PerceiverModel(\n",
              "    (input_preprocessor): PerceiverTextPreprocessor(\n",
              "      (embeddings): Embedding(262, 768)\n",
              "      (position_embeddings): Embedding(2048, 768)\n",
              "    )\n",
              "    (embeddings): PerceiverEmbeddings()\n",
              "    (encoder): PerceiverEncoder(\n",
              "      (cross_attention): PerceiverLayer(\n",
              "        (attention): PerceiverAttention(\n",
              "          (self): PerceiverSelfAttention(\n",
              "            (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (layernorm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=256, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=1280, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): PerceiverSelfOutput(\n",
              "            (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): PerceiverMLP(\n",
              "          (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "        )\n",
              "      )\n",
              "      (self_attends): ModuleList(\n",
              "        (0): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (1): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (2): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (3): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (4): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (5): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (6): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (7): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (8): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (9): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (10): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (11): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (12): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (13): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (14): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (15): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (16): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (17): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (18): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (19): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (20): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (21): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (22): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (23): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (24): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (25): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): Identity()\n",
              "              (query): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (decoder): PerceiverClassificationDecoder(\n",
              "      (decoder): PerceiverBasicDecoder(\n",
              "        (output_position_encodings): PerceiverTrainablePositionEncoding()\n",
              "        (positions_projection): Identity()\n",
              "        (decoding_cross_attention): PerceiverLayer(\n",
              "          (attention): PerceiverAttention(\n",
              "            (self): PerceiverSelfAttention(\n",
              "              (layernorm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (layernorm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (query): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (key): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (value): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): PerceiverSelfOutput(\n",
              "              (dense): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "          )\n",
              "          (layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): PerceiverMLP(\n",
              "            (dense1): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "            (dense2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (final_layer): Linear(in_features=1280, out_features=2, bias=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5P3jGwTMd29s"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "Here we train the model using native PyTorch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "aFtlQAxeddc-",
        "outputId": "2b6104c8-be45-424b-8fed-01a2b76c812c"
      },
      "source": [
        "from transformers import AdamW\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "model.train()\n",
        "for epoch in range(20):  # loop over the dataset multiple times\n",
        "    print(\"Epoch:\", epoch)\n",
        "    for batch in tqdm(train_dataloader):\n",
        "         # get the inputs; \n",
        "         inputs = batch[\"input_ids\"].to(device)\n",
        "         attention_mask = batch[\"attention_mask\"].to(device)\n",
        "         labels = batch[\"label\"].to(device)\n",
        "\n",
        "         # zero the parameter gradients\n",
        "         optimizer.zero_grad()\n",
        "\n",
        "         # forward + backward + optimize\n",
        "         outputs = model(inputs=inputs, attention_mask=attention_mask, labels=labels)\n",
        "         loss = outputs.loss\n",
        "         loss.backward()\n",
        "         optimizer.step()\n",
        "\n",
        "         # evaluate\n",
        "         predictions = outputs.logits.argmax(-1).cpu().detach().numpy()\n",
        "         accuracy = accuracy_score(y_true=batch[\"label\"].numpy(), y_pred=predictions)\n",
        "         print(f\"Loss: {loss.item()}, Accuracy: {accuracy}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n"
          ]
        },
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          "text": [
            "Loss: 0.8304584622383118, Accuracy: 0.25\n",
            "Loss: 0.9324597120285034, Accuracy: 0.75\n",
            "Loss: 2.6381170749664307, Accuracy: 0.0\n",
            "Loss: 0.7451722025871277, Accuracy: 0.5\n",
            "Loss: 0.7533012628555298, Accuracy: 0.5\n",
            "Epoch: 1\n"
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          "text": [
            "Loss: 1.2992808818817139, Accuracy: 0.25\n",
            "Loss: 1.0224494934082031, Accuracy: 0.25\n",
            "Loss: 0.7040843963623047, Accuracy: 0.25\n",
            "Loss: 0.6907804012298584, Accuracy: 0.5\n",
            "Loss: 0.7203900814056396, Accuracy: 0.25\n",
            "Epoch: 2\n"
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          "text": [
            "Loss: 0.5046183466911316, Accuracy: 1.0\n",
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            "Loss: 0.938153862953186, Accuracy: 0.5\n",
            "Loss: 0.9919759035110474, Accuracy: 0.25\n",
            "Loss: 0.686953604221344, Accuracy: 0.5\n",
            "Epoch: 3\n"
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            "Loss: 0.5484501719474792, Accuracy: 0.75\n",
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            "Loss: 0.551606297492981, Accuracy: 0.75\n",
            "Loss: 0.6901388764381409, Accuracy: 0.5\n",
            "Epoch: 4\n"
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            "Epoch: 5\n"
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            "Loss: 1.3074119091033936, Accuracy: 0.5\n",
            "Epoch: 6\n"
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            "Loss: 0.3487512767314911, Accuracy: 0.75\n",
            "Loss: 0.9425325989723206, Accuracy: 0.25\n",
            "Epoch: 7\n"
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            "Loss: 1.2848037481307983, Accuracy: 0.25\n",
            "Loss: 0.5453243255615234, Accuracy: 0.75\n",
            "Epoch: 8\n"
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            "Loss: 0.04499734565615654, Accuracy: 1.0\n",
            "Loss: 0.015084860846400261, Accuracy: 1.0\n",
            "Loss: 0.42504045367240906, Accuracy: 0.75\n",
            "Loss: 0.4822450280189514, Accuracy: 0.75\n",
            "Loss: 5.076355934143066, Accuracy: 0.25\n",
            "Epoch: 17\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b2c702ec0e74a1880a02549456bae46",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 2.05873966217041, Accuracy: 0.25\n",
            "Loss: 0.08333556354045868, Accuracy: 1.0\n",
            "Loss: 1.191737174987793, Accuracy: 0.5\n",
            "Loss: 1.0786267518997192, Accuracy: 0.5\n",
            "Loss: 0.743228554725647, Accuracy: 0.5\n",
            "Epoch: 18\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b2d306a5c3643f38bd77e83a55c5881",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.5613749027252197, Accuracy: 0.75\n",
            "Loss: 0.3839970827102661, Accuracy: 0.75\n",
            "Loss: 1.4717833995819092, Accuracy: 0.25\n",
            "Loss: 0.9521416425704956, Accuracy: 0.25\n",
            "Loss: 0.40143632888793945, Accuracy: 0.75\n",
            "Epoch: 19\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a70773bbac240099db03e7c823edef3",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.640500009059906, Accuracy: 0.75\n",
            "Loss: 0.34529292583465576, Accuracy: 1.0\n",
            "Loss: 0.16599130630493164, Accuracy: 1.0\n",
            "Loss: 0.49584537744522095, Accuracy: 0.75\n",
            "Loss: 0.42844119668006897, Accuracy: 0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcsR-ct94W1h"
      },
      "source": [
        "## Evaluate the model\n",
        "\n",
        "Finally, we evaluate the model on the test set. We use the Datasets library to compute the accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkQjXLMx2ZJn"
      },
      "source": [
        "from tqdm.notebook import tqdm\n",
        "from datasets import load_metric\n",
        "\n",
        "accuracy = load_metric(\"accuracy\")\n",
        "\n",
        "model.eval()\n",
        "for batch in tqdm(test_dataloader):\n",
        "      # get the inputs; \n",
        "      inputs = batch[\"input_ids\"].to(device)\n",
        "      attention_mask = batch[\"attention_mask\"].to(device)\n",
        "      labels = batch[\"label\"].to(device)\n",
        "\n",
        "      # forward pass\n",
        "      outputs = model(inputs=inputs, attention_mask=attention_mask)\n",
        "      logits = outputs.logits \n",
        "      predictions = logits.argmax(-1).cpu().detach().numpy()\n",
        "      references = batch[\"label\"].numpy()\n",
        "      accuracy.add_batch(predictions=predictions, references=references)\n",
        "\n",
        "final_score = accuracy.compute()\n",
        "print(\"Accuracy on test set:\", final_score)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liX2IdFPfYix"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbVxJYSNfGvS"
      },
      "source": [
        "text = \"I loved this movie, it's super good.\"\n",
        "\n",
        "input_ids = tokenizer(text, return_tensors=\"pt\").input_ids\n",
        "\n",
        "# forward pass\n",
        "outputs = model(inputs=input_ids.to(device))\n",
        "logits = outputs.logits \n",
        "predicted_class_idx = logits.argmax(-1).item()\n",
        "\n",
        "print(\"Predicted:\", model.config.id2label[predicted_class_idx])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33ll9igqfrqk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}